REAL-TIME FAULTS DIAGNOSIS IN A LARGE-SCALE SYSTEM USING NEURAL NETWORKS BASED ON SIGNED GAIN DIGRAPH WITH APPLICATION

Abstract In this paper, a novel intelligent control is proposed based on Artificial Neural Networks (ANN) to mitigate cascading failure (CF) and prevent blackout in smart grid systems after N-1-1 contingency condition in real-time. The fundamental contribution of this research is to deploy the machine learning concept for preventing blackout at early stages of its occurrence and to make smart grids more resilient, reliable, and robust. The proposed method provides the best action selection strategy for adaptive adjustment of generators’ output power through frequency control. This method is able to relieve congestion of transmission lines and prevent consecutive transmission line outage after N-1-1 contingency condition. The proposed ANN-based control approach is tested on an experimental 100 kW test system developed by the authors to test intelligent systems. Additionally, the proposed approach is validated on the large-scale IEEE 118-bus power system by simulation studies. Experimental results show that the ANN approach is very promising and provides accurate and robust control by preventing blackout. The technique is compared to a heuristic multi-agent system (MAS) approach based on communication interchanges. The ANN approach showed more accurate and robust response than the MAS algorithm.

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NeuroSpike viewer: A graphical environment for efficient control, communication and display of large-scale real-time simulation of Spiking Neural Networks on embedded systems

IEEE Latin America Transactions ◽

10.1109/tla.2018.8408450 ◽

2018 ◽

Vol 16 (5) ◽

pp. 1524-1531

Author(s):

CARLOS DIAZ ◽

GIOVANNY RIVERA ◽

JUAN GERARDO AVALOS ◽

GABRIEL SANCHEZ ◽

GONZALO DUCHEN ◽

...

Keyword(s):

Neural Networks ◽

Embedded Systems ◽

Real Time ◽

Large Scale ◽

Spiking Neural Networks ◽

Real Time Simulation ◽

Graphical Environment ◽

Time Simulation ◽

Efficient Control

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A method for constructing real-time FEM-based simulator of stomach behavior with large-scale deformation by neural networks

10.1117/12.911171 ◽

2012 ◽

Cited By ~ 3

Author(s):

Ken'ichi Morooka ◽

Tomoyuki Taguchi ◽

Xian Chen ◽

Ryo Kurazume ◽

Makoto Hashizume ◽

...

Keyword(s):

Neural Networks ◽

Real Time ◽

Large Scale

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FPGA implementation of deep-learning recurrent neural networks with sub-millisecond real-time latency for BCI-decoding of large-scale neural sensors (104 nodes)

2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) ◽

10.1109/embc.2018.8512415 ◽

2018 ◽

Cited By ~ 4

Author(s):

C Heelan ◽

AV Nurmikko ◽

W Truccolo

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Real Time ◽

Recurrent Neural Networks ◽

Large Scale ◽

Fpga Implementation ◽

Time Latency

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A wavefront array processing architecture for real-time simulation of large scale neural networks

Proceedings of 1993 International Conference on Neural Networks (IJCNN-93-Nagoya, Japan) ◽

10.1109/ijcnn.1993.717040 ◽

2005 ◽

Author(s):

Myung Won Kim ◽

Youngjik Lee ◽

Chong Moon Kim ◽

Yoonseon Song

Keyword(s):

Neural Networks ◽

Real Time ◽

Large Scale ◽

Array Processing ◽

Real Time Simulation ◽

Time Simulation ◽

Processing Architecture

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Segmentation of Illuminated Areas of Light Using CNN and Large-Scale RGB+D Dataset for Augmented and Mixed Reality Systems

10.51130/graphicon-2020-2-4-58 ◽

2020 ◽

pp. short58-1-short58-7

Author(s):

Maksim Sorokin ◽

Dmitriy Zhdanov ◽

Andrey Zhdanov

Keyword(s):

Neural Networks ◽

Real Time ◽

Convolutional Neural Networks ◽

Large Scale ◽

Mixed Reality ◽

Depth Map ◽

Light Sources ◽

Realistic Rendering ◽

Visual Difference

This work is devoted to the problem of restoring realistic rendering for augmented and mixed reality systems. Finding the light sources and restoring the correct distribution of scene brightness is one of the key parameters that allows to solve the problem of correct interaction between the virtual and real worlds. With the advent of such datasets as, "LARGE-SCALE RGB + D," it became possible to train neural networks to recognize the depth map of images, which is a key requirement for working with the environment in real time. Additionally, in this work, convolutional neural networks were trained on the synthesized dataset with realistic lighting. The results of the proposed methods are presented, the accuracy of restoring the position of the light sources is estimated, and the visual difference between the image of the scene with the original light sources and the same scene. The speed allows it to be used in real-time AR/VR systems.

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